0b843baa4dada46b4841c000d835849aca95d81f8771a56269909ae346b7f2f0

README.md

@@ -19,27 +19,32 @@ metrics:
 </div>
 <!-- header end -->
 
-# Simply make AI models cheaper, smaller, faster, and greener!
-
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+[![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/CP4VSgck)
+
+# Simply make AI models cheaper, smaller, faster, and greener!
 
 - Give a thumbs up if you like this model!
 - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
 - Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
 - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
-- Share feedback and suggestions on the Slack of Pruna AI (Coming soon!).
+- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
 
 ## Results
 
 ![image info](./plots.png)
 
-These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in config.json. Results may vary in other settings (e.g. other hardware, image size, batch size, ...).
+**Important remarks:**
+- The quality of the model output might slightly vary compared to the base model. There might be minimal quality loss.
+- These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in config.json and are obtained after a hardware warmup. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...).
+- You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
 
 ## Setup
 
 You can run the smashed model with these steps:
+
 0. Check cuda, torch, packaging requirements are installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`. For packaging and torch, run `pip install packaging torch`.
 1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take 15 minutes to install.
     ```bash
@@ -72,9 +77,9 @@ You can run the smashed model with these steps:
 
 The configuration info are in `config.json`.
 
-## License
+## Credits & License
 
-We follow the same license as the original model. Please check the license of the original model CompVis-stable-diffusion-v1-4 before using this model.
+We follow the same license as the original model. Please check the license of the original model CompVis/stable-diffusion-v1-4 before using this model which provided the base model.
 
 ## Want to compress other models?
 

config.json

@@ -1 +1 @@
-{"pruners": "None", "pruning_ratio": 0.0, "factorizers": "None", "quantizers": "None", "n_quantization_bits": 32, "output_deviation": 0.005, "compilers": "['diffusers2']", "static_batch": true, "static_shape": false, "controlnet": "None", "unet_dim": 4, "device": "cuda", "save_dir": "/ceph/hdd/staff/charpent/models/.models/optimized_model", "batch_size": 1, "max_batch_size": 1, "image_height": 512, "image_width": 512, "version": "1.4", "task": "txt2img", "model_name": "CompVis/stable-diffusion-v1-4", "weight_name": "None", "save_load_fn": "stable_fast"}
+{"pruners": "None", "pruning_ratio": "None", "factorizers": "None", "quantizers": "None", "n_quantization_bits": 32, "output_deviation": 0.0, "compilers": "['step_caching', 'tiling', 'diffusers2']", "static_batch": true, "static_shape": false, "controlnet": "None", "unet_dim": 4, "device": "cuda", "batch_size": 1, "max_batch_size": 1, "image_height": 512, "image_width": 512, "version": "1.4", "task": "txt2img", "weight_name": "None", "model_name": "CompVis/stable-diffusion-v1-4", "save_load_fn": "stable_fast"}

model/smash_config.json

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d7f0574c6dbd9fe6b9bd3099a09b2ae640474eeee1bda5334f039f54bbdae188
-size 742
+oid sha256:3d8870f8c3dad4ed45677b4707444380dd1a84e0b05451a9f34bcc79048f9a67
+size 683